Modeling, Analysis, and Simulation On Computer and Telecommunication Systems 

About: Modeling, Analysis, and Simulation On Computer and Telecommunication Systems is an academic conference. The conference publishes majorly in the area(s): Cache & Server. Over the lifetime, 1549 publications have been published by the conference receiving 28512 citations.

BRITE: an approach to universal topology generation

Abstract: Effective engineering of the Internet is predicated upon a detailed understanding of issues such as the large-scale structure of its underlying physical topology, the manner in which it evolves over time, and the way in which its constituent components contribute to its overall function. Unfortunately, developing a deep understanding of these issues has proven to be a challenging task, since it in turn involves solving difficult problems such as mapping the actual topology, characterizing it, and developing models that capture its emergent behavior. Consequently, even though there are a number of topology models, it is an open question as to how representative the generated topologies they generate are of the actual Internet. Our goal is to produce a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness, and interoperability. Representativeness leads to synthetic topologies that accurately reflect many aspects of the actual Internet topology (e.g. hierarchical structure, node degree distribution, etc.). Inclusiveness combines the strengths of as many generation models as possible in a single generation tool. Interoperability provides interfaces to widely-used simulation applications such as ns and SSF and visualization tools like otter. We call such a tool a universal topology generator.

Energy-aware routing in cluster-based sensor networks

Abstract: There has been a growing interest in the applications of sensor networks. Since sensors are generally constrained in on-board energy supply, efficient management of the network is crucial in extending the life of the sensor. We present a novel approach for energy-aware and context-aware routing of sensor data. The approach calls for network clustering and assigns a less-energy-constrained gateway node that acts as a centralized network manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. Simulation results demonstrate that our approach can achieve substantial energy saving.

Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges

Abstract: Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station or a wired backbone network MANET nodes are typically distinguished by their limited power, processing, and memory resources as well as high degree of mobility In such networks, the wireless mobile nodes may dynamically enter the network as well as leave the network Due to the limited transmission range of wireless network nodes, multiple hops are usually needed for a node to exchange information with any other node in the network Thus routing is a crucial issue to the design of a MANET In this paper, we specifically examine the issues of multipath routing in MANETs Multipath routing allows the establishment of multiple paths between a single source and single destination node It is typically proposed in order to increase the reliability of data transmission (ie, fault tolerance) or to provide load balancing Load balancing is of especial importance in MANETs because of the limited bandwidth between the nodes We also discuss the application of multipath routing to support application constraints such as reliability, load-balancing, energy-conservation, and Quality-of-Service (QoS)

Extreme Binning: Scalable, parallel deduplication for chunk-based file backup

Abstract: Data deduplication is an essential and critical component of backup systems. Essential, because it reduces storage space requirements, and critical, because the performance of the entire backup operation depends on its throughput. Traditional backup workloads consist of large data streams with high locality, which existing deduplication techniques require to provide reasonable throughput. We present Extreme Binning, a scalable deduplication technique for non-traditional backup workloads that are made up of individual files with no locality among consecutive files in a given window of time. Due to lack of locality, existing techniques perform poorly on these workloads. Extreme Binning exploits file similarity instead of locality, and makes only one disk access for chunk lookup per file, which gives reasonable throughput. Multi-node backup systems built with Extreme Binning scale gracefully with the amount of input data; more backup nodes can be added to boost throughput. Each file is allocated using a stateless routing algorithm to only one node, allowing for maximum parallelization, and each backup node is autonomous with no dependency across nodes, making data management tasks robust with low overhead.

The Case for Evaluating MapReduce Performance Using Workload Suites

Abstract: MapReduce systems face enormous challenges due to increasing growth, diversity, and consolidation of the data and computation involved. Provisioning, configuring, and managing large-scale MapReduce clusters require realistic, workload-specific performance insights that existing MapReduce benchmarks are ill-equipped to supply. In this paper, we build the case for going beyond benchmarks for MapReduce performance evaluations. We analyze and compare two production MapReduce traces to develop a vocabulary for describing MapReduce workloads. We show that existing benchmarks fail to capture rich workload characteristics observed in traces, and propose a framework to synthesize and execute representative workloads. We demonstrate that performance evaluations using realistic workloads gives cluster operator new ways to identify workload-specific resource bottlenecks, and workload-specific choice of MapReduce task schedulers. We expect that once available, workload suites would allow cluster operators to accomplish previously challenging tasks beyond what we can now imagine, thus serving as a useful tool to help design and manage MapReduce systems.